fix

core/trainers/single_infer.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Training use fluid with one node only.
+"""
+
+from __future__ import print_function
+
+import time
+import logging
+import os
+import paddle.fluid as fluid
+
+from paddlerec.core.trainers.transpiler_trainer import TranspileTrainer
+from paddlerec.core.utils import envs
+from paddlerec.core.reader import SlotReader
+from paddlerec.core.utils import dataloader_instance
+
+logging.basicConfig(format="%(asctime)s - %(levelname)s - %(message)s")
+logger = logging.getLogger("fluid")
+logger.setLevel(logging.INFO)
+
+
+class SingleInfer(TranspileTrainer):
+    def __init__(self, config=None):
+        super(TranspileTrainer, self).__init__(config)
+        self._env = self._config
+        device = envs.get_global_env("device")
+        if device == 'gpu':
+            self._place = fluid.CUDAPlace(0)
+        elif device == 'cpu':
+            self._place = fluid.CPUPlace()
+        self._exe = fluid.Executor(self._place)
+        self.processor_register()
+        self._model = {}
+        self._dataset = {}
+        envs.set_global_envs(self._config)
+        envs.update_workspace()
+        self._runner_name = envs.get_global_env("mode")
+
+    def processor_register(self):
+        self.regist_context_processor('uninit', self.instance)
+        self.regist_context_processor('init_pass', self.init)
+        self.regist_context_processor('startup_pass', self.startup)
+        self.regist_context_processor('train_pass', self.executor_train)
+        self.regist_context_processor('terminal_pass', self.terminal)
+
+    def instance(self, context):
+        context['status'] = 'init_pass'
+
+    def _get_dataset(self, dataset_name):
+        name = "dataset." + dataset_name + "."
+        sparse_slots = envs.get_global_env(name + "sparse_slots")
+        dense_slots = envs.get_global_env(name + "dense_slots")
+        thread_num = envs.get_global_env(name + "thread_num")
+        batch_size = envs.get_global_env(name + "batch_size")
+        reader_class = envs.get_global_env(name + "data_converter")
+        abs_dir = os.path.dirname(os.path.abspath(__file__))
+        reader = os.path.join(abs_dir, '../utils', 'dataset_instance.py')
+
+        if sparse_slots is None and dense_slots is None:
+            pipe_cmd = "python {} {} {} {}".format(reader, reader_class,
+                                                   "TRAIN", self._config_yaml)
+        else:
+            if sparse_slots is None:
+                sparse_slots = "#"
+            if dense_slots is None:
+                dense_slots = "#"
+            padding = envs.get_global_env(name + "padding", 0)
+            pipe_cmd = "python {} {} {} {} {} {} {} {}".format(
+                reader, "slot", "slot", self._config_yaml, "fake", \
+                sparse_slots.replace(" ", "#"), dense_slots.replace(" ", "#"), str(padding))
+
+        dataset = fluid.DatasetFactory().create_dataset()
+        dataset.set_batch_size(envs.get_global_env(name + "batch_size"))
+        dataset.set_pipe_command(pipe_cmd)
+        train_data_path = envs.get_global_env(name + "data_path")
+        file_list = [
+            os.path.join(train_data_path, x)
+            for x in os.listdir(train_data_path)
+        ]
+        dataset.set_filelist(file_list)
+        for model_dict in self._env["phase"]:
+            if model_dict["dataset_name"] == dataset_name:
+                model = self._model[model_dict["name"]][3]
+                inputs = model._infer_data_var
+                dataset.set_use_var(inputs)
+                break
+        return dataset
+
+    def _get_dataloader(self, dataset_name, dataloader):
+        name = "dataset." + dataset_name + "."
+        sparse_slots = envs.get_global_env(name + "sparse_slots")
+        dense_slots = envs.get_global_env(name + "dense_slots")
+        thread_num = envs.get_global_env(name + "thread_num")
+        batch_size = envs.get_global_env(name + "batch_size")
+        reader_class = envs.get_global_env(name + "data_converter")
+        abs_dir = os.path.dirname(os.path.abspath(__file__))
+        if sparse_slots is None and dense_slots is None:
+            reader = dataloader_instance.dataloader_by_name(
+                reader_class, dataset_name, self._config_yaml)
+            reader_class = envs.lazy_instance_by_fliename(reader_class,
+                                                          "TrainReader")
+            reader_ins = reader_class(self._config_yaml)
+        else:
+            reader = dataloader_instance.slotdataloader_by_name(
+                "", dataset_name, self._config_yaml)
+            reader_ins = SlotReader(self._config_yaml)
+        if hasattr(reader_ins, 'generate_batch_from_trainfiles'):
+            dataloader.set_sample_list_generator(reader)
+        else:
+            dataloader.set_sample_generator(reader, batch_size)
+        return dataloader
+
+    def _create_dataset(self, dataset_name):
+        name = "dataset." + dataset_name + "."
+        sparse_slots = envs.get_global_env(name + "sparse_slots")
+        dense_slots = envs.get_global_env(name + "dense_slots")
+        thread_num = envs.get_global_env(name + "thread_num")
+        batch_size = envs.get_global_env(name + "batch_size")
+        type_name = envs.get_global_env(name + "type")
+        if envs.get_platform() != "LINUX":
+            print("platform ", envs.get_platform(),
+                  " change reader to DataLoader")
+            type_name = "DataLoader"
+        padding = 0
+
+        if type_name == "DataLoader":
+            return None
+        else:
+            return self._get_dataset(dataset_name)
+
+    def init(self, context):
+        for model_dict in self._env["phase"]:
+            self._model[model_dict["name"]] = [None] * 5
+            train_program = fluid.Program()
+            startup_program = fluid.Program()
+            scope = fluid.Scope()
+            dataset_name = model_dict["dataset_name"]
+            opt_name = envs.get_global_env("hyper_parameters.optimizer.class")
+            opt_lr = envs.get_global_env(
+                "hyper_parameters.optimizer.learning_rate")
+            opt_strategy = envs.get_global_env(
+                "hyper_parameters.optimizer.strategy")
+            with fluid.program_guard(train_program, startup_program):
+                with fluid.unique_name.guard():
+                    with fluid.scope_guard(scope):
+                        model_path = model_dict["model"].replace(
+                            "{workspace}",
+                            envs.path_adapter(self._env["workspace"]))
+                        model = envs.lazy_instance_by_fliename(
+                            model_path, "Model")(self._env)
+                        model._infer_data_var = model.input_data(
+                            dataset_name=model_dict["dataset_name"])
+                        if envs.get_global_env("dataset." + dataset_name +
+                                               ".type") == "DataLoader":
+                            model._init_dataloader(is_infer=True)
+                            self._get_dataloader(dataset_name,
+                                                 model._data_loader)
+                        model.net(model._infer_data_var, True)
+            self._model[model_dict["name"]][0] = train_program
+            self._model[model_dict["name"]][1] = startup_program
+            self._model[model_dict["name"]][2] = scope
+            self._model[model_dict["name"]][3] = model
+            self._model[model_dict["name"]][4] = train_program.clone()
+
+        for dataset in self._env["dataset"]:
+            if dataset["type"] != "DataLoader":
+                self._dataset[dataset["name"]] = self._create_dataset(dataset[
+                    "name"])
+
+        context['status'] = 'startup_pass'
+
+    def startup(self, context):
+        for model_dict in self._env["phase"]:
+            with fluid.scope_guard(self._model[model_dict["name"]][2]):
+                self._exe.run(self._model[model_dict["name"]][1])
+        context['status'] = 'train_pass'
+
+    def executor_train(self, context):
+        epochs = int(self._env["epochs"])
+        for j in range(epochs):
+            for model_dict in self._env["phase"]:
+                if j == 0:
+                    with fluid.scope_guard(self._model[model_dict["name"]][2]):
+                        train_prog = self._model[model_dict["name"]][0]
+                        startup_prog = self._model[model_dict["name"]][1]
+                        with fluid.program_guard(train_prog, startup_prog):
+                            self.load()
+                reader_name = model_dict["dataset_name"]
+                name = "dataset." + reader_name + "."
+                begin_time = time.time()
+                if envs.get_global_env(name + "type") == "DataLoader":
+                    self._executor_dataloader_train(model_dict)
+                else:
+                    self._executor_dataset_train(model_dict)
+                with fluid.scope_guard(self._model[model_dict["name"]][2]):
+                    train_prog = self._model[model_dict["name"]][4]
+                    startup_prog = self._model[model_dict["name"]][1]
+                    with fluid.program_guard(train_prog, startup_prog):
+                        self.save(j)
+                end_time = time.time()
+                seconds = end_time - begin_time
+            print("epoch {} done, time elasped: {}".format(j, seconds))
+        context['status'] = "terminal_pass"
+
+    def _executor_dataset_train(self, model_dict):
+        reader_name = model_dict["dataset_name"]
+        model_name = model_dict["name"]
+        model_class = self._model[model_name][3]
+        fetch_vars = []
+        fetch_alias = []
+        fetch_period = 20
+        metrics = model_class.get_infer_results()
+        if metrics:
+            fetch_vars = metrics.values()
+            fetch_alias = metrics.keys()
+        scope = self._model[model_name][2]
+        program = self._model[model_name][0]
+        reader = self._dataset[reader_name]
+        with fluid.scope_guard(scope):
+            self._exe.infer_from_dataset(
+                program=program,
+                dataset=reader,
+                fetch_list=fetch_vars,
+                fetch_info=fetch_alias,
+                print_period=fetch_period)
+
+    def _executor_dataloader_train(self, model_dict):
+        reader_name = model_dict["dataset_name"]
+        model_name = model_dict["name"]
+        model_class = self._model[model_name][3]
+        program = self._model[model_name][0].clone()
+        fetch_vars = []
+        fetch_alias = []
+        fetch_period = 20
+        metrics = model_class.get_infer_results()
+        if metrics:
+            fetch_vars = metrics.values()
+            fetch_alias = metrics.keys()
+        metrics_varnames = []
+        metrics_format = []
+        fetch_period = 20
+        metrics_format.append("{}: {{}}".format("batch"))
+        for name, var in metrics.items():
+            metrics_varnames.append(var.name)
+            metrics_format.append("{}: {{}}".format(name))
+        metrics_format = ", ".join(metrics_format)
+
+        reader = self._model[model_name][3]._data_loader
+        reader.start()
+        batch_id = 0
+        scope = self._model[model_name][2]
+        with fluid.scope_guard(scope):
+            try:
+                while True:
+                    metrics_rets = self._exe.run(program=program,
+                                                 fetch_list=metrics_varnames)
+                    metrics = [batch_id]
+                    metrics.extend(metrics_rets)
+
+                    if batch_id % fetch_period == 0 and batch_id != 0:
+                        print(metrics_format.format(*metrics))
+                    batch_id += 1
+            except fluid.core.EOFException:
+                reader.reset()
+
+    def terminal(self, context):
+        context['is_exit'] = True
+
+    def load(self, is_fleet=False):
+        name = "runner." + self._runner_name + "."
+        dirname = envs.get_global_env("epoch.init_model_path", None)
+        if dirname is None:
+            return
+        print("single_infer going to load ", dirname)
+        if is_fleet:
+            fleet.load_persistables(self._exe, dirname)
+        else:
+            fluid.io.load_persistables(self._exe, dirname)
+
+    def save(self, epoch_id, is_fleet=False):
+        def need_save(epoch_id, epoch_interval, is_last=False):
+            if is_last:
+                return True
+            if epoch_id == -1:
+                return False
+
+            return epoch_id % epoch_interval == 0
+
+        def save_inference_model():
+            name = "runner." + self._runner_name + "."
+            save_interval = int(
+                envs.get_global_env(name + "save_inference_interval", -1))
+            if not need_save(epoch_id, save_interval, False):
+                return
+            feed_varnames = envs.get_global_env(
+                name + "save_inference_feed_varnames", None)
+            fetch_varnames = envs.get_global_env(
+                name + "save_inference_fetch_varnames", None)
+            if feed_varnames is None or fetch_varnames is None or feed_varnames == "":
+                return
+            fetch_vars = [
+                fluid.default_main_program().global_block().vars[varname]
+                for varname in fetch_varnames
+            ]
+            dirname = envs.get_global_env(name + "save_inference_path", None)
+
+            assert dirname is not None
+            dirname = os.path.join(dirname, str(epoch_id))
+
+            if is_fleet:
+                fleet.save_inference_model(self._exe, dirname, feed_varnames,
+                                           fetch_vars)
+            else:
+                fluid.io.save_inference_model(dirname, feed_varnames,
+                                              fetch_vars, self._exe)
+
+        def save_persistables():
+            name = "runner." + self._runner_name + "."
+            save_interval = int(
+                envs.get_global_env(name + "save_checkpoint_interval", -1))
+            if not need_save(epoch_id, save_interval, False):
+                return
+            dirname = envs.get_global_env(name + "save_checkpoint_path", None)
+            if dirname is None or dirname == "":
+                return
+            dirname = os.path.join(dirname, str(epoch_id))
+            if is_fleet:
+                fleet.save_persistables(self._exe, dirname)
+            else:
+                fluid.io.save_persistables(self._exe, dirname)
+
+        save_persistables()
+        save_inference_model()